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# Who cites «Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework»

42 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/white-2017-inference-is-everything-recasting-semantic/)

1. [On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs](https://aclanthology.org/2025.naacl-long.29/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
2. [Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logs](https://aclanthology.org/2025.acl-long.161/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [CoELM: Construction-Enhanced Language Modeling](https://aclanthology.org/2024.acl-long.542/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task](https://arxiv.org/abs/2304.09138) *— 2023 · arXiv:2304.09138*
5. [Maximum Entropy Loss, the Silver Bullet Targeting Backdoor Attacks in Pre-trained Language Models](https://aclanthology.org/2023.findings-acl.237/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
6. [What do Language Models know about word senses? Zero-Shot WSD with Language Models and Domain Inventories](https://aclanthology.org/2023.gwc-1.40/) *— 2023 · Proceedings of the 12th Global Wordnet Conference*
7. [When and Why Does Bias Mitigation Work?](https://aclanthology.org/2023.findings-emnlp.619/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
8. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
9. [Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding](https://aclanthology.org/2022.naacl-main.234/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
10. [On Measuring the Intrinsic Few-Shot Hardness of Datasets](https://aclanthology.org/2022.emnlp-main.262/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
11. [Partial-input baselines show that NLI models can ignore context, but they don't.](https://aclanthology.org/2022.naacl-main.350/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
12. [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning](https://aclanthology.org/2022.findings-naacl.187/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
13. [Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference](https://aclanthology.org/2022.tacl-1.35/) *— 2022*
14. [Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference](https://arxiv.org/abs/2101.07397) *— 2021 · arXiv:2101.07397*
15. [Extracting and filtering paraphrases by bridging natural language inference and paraphrasing](https://arxiv.org/abs/2111.07119) *— 2021 · arXiv:2111.07119*
16. [Figurative Language in Recognizing Textual Entailment](https://aclanthology.org/2021.findings-acl.297/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
17. [A guide to the dataset explosion in QA, NLI, and commonsense reasoning](https://md.mova.elamur.ai/free/en/docs/papers/rogers-2020-guide-dataset-explosion-qa-nli/) *— our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts*
18. [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://md.mova.elamur.ai/free/en/docs/papers/jeretic-2020-are-natural-language-inference-models/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
19. [ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://aclanthology.org/2020.emnlp-main.661/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
20. [End-to-End Bias Mitigation by Modelling Biases in Corpora](https://aclanthology.org/2020.acl-main.769/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
21. [Frequency, acceptability, and selection: A case study of clause-embedding](https://www.glossa-journal.org/article/id/5353/) *— 2020 · Glossa: a journal of general linguistics*
22. [Temporal Reasoning in Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/vashishtha-2020-temporal-reasoning-natural-language-inference/) *— our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
23. [Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling](https://aclanthology.org/P19-1439/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
24. [Don''t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://aclanthology.org/P19-1084/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
25. [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://aclanthology.org/P19-1513/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
26. [Inherent Disagreements in Human Textual Inferences](https://md.mova.elamur.ai/free/en/docs/papers/pavlick-2019-inherent-disagreements-human-textual-inferences/) *— our card · 2019*
27. [Probing Natural Language Inference Models through Semantic Fragments](https://arxiv.org/abs/1909.07521) *— 2019 · arXiv:1909.07521*
28. [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://aclanthology.org/S19-1026/) *— 2019 · Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)*
29. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
30. [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/) *— our card · 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
31. [What do you learn from context? Probing for sentence structure in contextualized word representations](https://arxiv.org/abs/1905.06316) *— 2019 · arXiv:1905.06316*
32. [Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness](https://aclanthology.org/N18-1179/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
33. [Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://aclanthology.org/D18-1007/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
34. [Lexicosyntactic Inference in Neural Models](https://aclanthology.org/D18-1501/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
35. [On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference](https://aclanthology.org/N18-2082/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)*
36. [Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks](https://arxiv.org/abs/1811.01088) *— 2018 · arXiv:1811.01088*
37. [Transforming Question Answering Datasets Into Natural Language Inference Datasets](https://arxiv.org/abs/1809.02922) *— 2018 · arXiv:1809.02922*
38. elazar-2020-extraordinary-failure-complement-coercion-crowdsourcing
39. he-2023-multimodal-graph-transformer-multimodal-question
40. stacey-2020-avoiding-hypothesis-only-bias-natural
41. sumita-2021-talking-theorem-prover-interactively-solve
42. wu-2021-variation-generality-encoding-syntactic-anomaly
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